Hallucination refers to the inaccurate, irrelevant, and inconsistent text generated from large language models (LLMs). While the LLMs have shown great promise in a variety of tasks, the issue of hallucination still remains a major challenge for many practical uses. In this paper, we tackle the issue of hallucination in abstract text summarization by mitigating exposure bias. Existing models targeted for exposure bias mitigation, namely BRIO, aim for better summarization quality in the ROUGE score. We propose a model that uses a similar exposure bias mitigation strategy but with a goal that is aligned with less hallucination. We conjecture that among a group of candidate outputs, ones with hallucinations will comprise the minority of the whole group. That is, candidates with less similarity with others will have a higher chance of containing hallucinated content. Our method uses this aspect and utilizes contrastive learning, incentivizing candidates with high inter-candidate ROUGE scores. We performed experiments on the XSum and CNN/DM summarization datasets, and our method showed 6.25% and 3.82% improvement, respectively, on the consistency G-Eval score over BRIO.

翻译：幻觉是指大型语言模型（LLM）生成的文本存在不准确、不相关或不一致的问题。尽管LLM在多种任务中展现出巨大潜力，但幻觉问题仍然是许多实际应用面临的主要挑战。本文通过缓解曝光偏差来解决抽象文本摘要中的幻觉问题。现有针对曝光偏差缓解的模型（即BRIO）旨在提高ROUGE分数方面的摘要质量。我们提出一种采用类似曝光偏差缓解策略的模型，但其目标更侧重于减少幻觉。我们推测，在一组候选输出中，包含幻觉的文本将占整体少数。也就是说，与其他候选文本相似度较低的文本更可能包含幻觉内容。我们的方法利用这一特性，采用对比学习机制，激励具有高候选间ROUGE分数的候选文本。我们在XSum和CNN/DM摘要数据集上进行了实验，结果表明，在一致性G-Eval分数上，我们的方法相比BRIO分别提升了6.25%和3.82%。